Quande Qin

Brain storm optimization algorithm: a review

For swarm intelligence algorithms, each individual in the swarm represents a solution in the search space, and it also can be seen as a data sample from the search space. Based on the analyses of these data, more effective algorithms and search strategies could be proposed. Brain storm optimization (BSO) algorithm is a new and promising swarm intelligence algorithm, which simulates the human brainstorming process. Through the convergent operation and divergent operation, individuals in BSO are grouped and diverged in the search space/objective space. In this paper, the history development, and the state-of-the-art of the BSO algorithm are reviewed. In addition, the convergent operation and divergent operation in the BSO algorithm are also discussed from the data analysis perspective. Every individual in the BSO algorithm is not only a solution to the problem to be optimized, but also a data point to reveal the landscape of the problem. Swarm intelligence and data mining techniques can be combined to produce benefits above and beyond what either method could achieve alone.

Particle Swarm Optimization With Interswarm Interactive Learning Strategy

The learning strategy in the canonical particle swarm optimization (PSO) algorithm is often blamed for being the primary reason for loss of diversity. Population diversity maintenance is crucial for preventing particles from being stuck into local optima. In this paper, we present an improved PSO algorithm with an interswarm interactive learning strategy (IILPSO) by overcoming the drawbacks of the canonical PSO algorithms learning strategy. IILPSO is inspired by the phenomenon in human society that the interactive learning behavior takes place among different groups. Particles in IILPSO are divided into two swarms. The interswarm interactive learning (IIL) behavior is triggered when the best particles fitness value of both the swarms does not improve for a certain number of iterations. According to the best particles fitness value of each swarm, the softmax method and roulette method are used to determine the roles of the two swarms as the learning swarm and the learned swarm. In addition, the velocity mutation operator and global best vibration strategy are used to improve the algorithms global search capability. The IIL strategy is applied to PSO with global star and local ring structures, which are termed as IILPSO-G and IILPSO-L algorithm, respectively. Numerical experiments are conducted to compare the proposed algorithms with eight popular PSO variants. From the experimental results, IILPSO demonstrates the good performance in terms of solution accuracy, convergence speed, and reliability. Finally, the variations of the population diversity in the entire search process provide an explanation why IILPSO performs effectively.

Multiple strategies based orthogonal design particle swarm optimizer for numerical optimization

In the canonical particle swarm optimization (PSO), each particle updates its velocity and position by taking its historical best experience and its neighbors? best experience as exemplars and adding them together. Its performance is largely dependent on the employed exemplars. However, this learning strategy in the canonical PSO is inefficient when complex problems are being optimized. In this paper, Multiple Strategies based Orthogonal Design PSO (MSODPSO) is presented, in which the social-only model or the cognition-only model is utilized in each particle?s velocity update, and an orthogonal design (OD) method is used with a small probability to construct a new exemplar in each iteration. In order to enhance the efficiency of OD method and obtain more efficient exemplar, four auxiliary vector generating strategies are designed. In addition, a global best mutation operator including non-uniform mutation and Gaussian mutation is employed to improve its global search ability. The MSODPSO can be applied to PSO with the global or local structure, yielding MSODPSO-G and MSODPSO-L algorithms, respectively. To verify the effectiveness of the proposed algorithms, a set of 24 benchmark functions in 30 and 100 dimensions are utilized in experimental studies. The proposed algorithm is also tested on a real-world economic load dispatch (ELD) problem, which is modelled as a non-convex minimization problem with constraints. The experimental results on the benchmark functions and ELD problems demonstrate that the proposed MSODPSO-G and MSODPSO-L can offer high-quality solutions.

Population Diversity of Particle Swarm Optimizer Solving Single and Multi-Objective Problems

Premature convergence occurs in swarm intelligence algorithms searching for optima. A swarm intelligence algorithm has two kinds of abilities: exploration of new possibilities and exploitation of old certainties. The exploration ability means that an algorithm can explore more search place to increase the possibility that the algorithm can find good enough solutions. In contrast, the exploitation ability means that an algorithm focuses on the refinement of found promising areas. An algorithm should have a balance between exploration and exploitation, that is, the allocation of computational resources should be optimized to ensure that an algorithm can find good enough solutions effectively. The diversity measures the distribution of individuals’ information. From the observation of the distribution and diversity change, the degree of exploration and exploitation can be obtained. Another issue in multiobjective is the solution metric. Pareto domination is utilized to compare between two solutions, however, solutions are almost Pareto non-dominated for multi-objective problems with more than ten objectives. In this paper, the authors analyze the population diversity of particle swarm optimizer for solving both single objective and multiobjective problems. The population diversity of solutions is used to measure the goodness of a set of solutions. This metric may guide the search in problems with numerous objectives. Adaptive optimization algorithms can be designed through controlling the balance between exploration and exploitation.

Population Diversity Maintenance In Brain Storm Optimization Algorithm

Abstract The convergence and divergence are two common phenomena in swarm intelligence. To obtain good search results, the algorithm should have a balance on convergence and divergence. The premature convergence happens partially due to the solutions getting clustered together, and not diverging again. The brain storm optimization (BSO), which is a young and promising algorithm in swarm intelligence, is based on the collective behavior of human being, that is, the brainstorming process. The convergence strategy is utilized in BSO algorithm to exploit search areas may contain good solutions. The new solutions are generated by divergence strategy to explore new search areas. Premature convergence also happens in the BSO algorithm. The solutions get clustered after a few iterations, which indicate that the population diversity decreases quickly during the search. A definition of population diversity in BSO algorithm is introduced in this paper to measure the change of solutions’ distribution. The algorithms exploration and exploitation ability can be measured based on the change of population diversity. Different kinds of partial reinitialization strategies are utilized to improve the population diversity in BSO algorithm. The experimental results show that the performance of the BSO is improved by part of solutions re-initialization strategies.

Swarm Intelligence in Big Data Analytics

This paper analyses the difficulty of big data analytics problems and the potential of swarm intelligence solving big data analytics problems. Nowadays, the big data analytics has attracted more and more attentions, which is required to manage immense amounts of data quickly. However, current researches mainly focus on the amount of data. In this paper, the other three properties of big data analytics, which include the high dimensionality of data, the dynamical change of data, and the multi-objective of problems, are discussed. Swarm intelligence, which works with a population of individuals, is a collection of nature-inspired searching techniques. It has effectively solved many large-scale, dynamical, and multi-objective problems. Based on the combination of swarm intelligence and data mining techniques, we can have better understanding of the big data analytics problems, and designing more effective algorithms to solve real-world big data analytics problems.

Promoting diversity in particle swarm optimization to solve multimodal problems

Promoting diversity is an effective way to prevent premature converge in solving multimodal problems using Particle Swarm Optimization (PSO). Based on the idea of increasing possibility of particles “jump out” of local optima, while keeping the ability of algorithm finding “good enough” solution, two methods are utilized to promote PSOs diversity in this paper. PSO population diversity measurements, which include position diversity, velocity diversity and cognitive diversity on standard PSO and PSO with diversity promotion, are discussed and compared. Through this measurement, useful information of search in exploration or exploitation state can be obtained.

A Study of Normalized Population Diversity in Particle Swarm Optimization

The values and velocities of a Particle swarm optimization (PSO) algorithm can be recorded as series of matrix and its population diversity can be considered as an observation of the distribution of matrix elements. Each dimension is measured separately in the dimension-wise diversity, on the contrary, the element-wise diversity measures all dimension together. In this paper, PSO algorithm is first represented in the matrix format, then based on the analysis of the relationship between pairs of vectors in PSO solution matrix, different normalization strategies are utilized for dimension-wise and element-wise population diversity, respectively. Experiments on benchmark functions are conducted. Based on the simulation results of ten benchmark functions (include unimodal/multimodal function, separable/non-separable function), the properties of normalized population diversities are analyzed and discussed.

Big data analytics with swarm intelligence

– The quality and quantity of data are vital for the effectiveness of problem solving. Nowadays, big data analytics, which require managing an immense amount of data rapidly, has attracted more and more attention. It is a new research area in the field of information processing techniques. It faces the big challenges and difficulties of a large amount of data, high dimensionality, and dynamical change of data. However, such issues might be addressed with the help from other research fields, e.g., swarm intelligence (SI), which is a collection of nature-inspired searching techniques. The paper aims to discuss these issues. , – In this paper, the potential application of SI in big data analytics is analyzed. The correspondence and association between big data analytics and SI techniques are discussed. As an example of the application of the SI algorithms in the big data processing, a commodity routing system in a port in China is introduced. Another example is the economic load dispatch problem in the planning of a modern power system. , – The characteristics of big data include volume, variety, velocity, veracity, and value. In the SI algorithms, these features can be, respectively, represented as large scale, high dimensions, dynamical, noise/surrogates, and fitness/objective problems, which have been effectively solved. , – In current research, the example problem of the port is formulated but not solved yet given the ongoing nature of the project. The example could be understood as advanced IT or data processing technology, however, its underlying mechanism could be the SI algorithms. This paper is the first step in the research to utilize the SI algorithm to a big data analytics problem. The future research will compare the performance of the method and fit it in a dynamic real system. , – Based on the combination of SI and data mining techniques, the authors can have a better understanding of the big data analytics problems, and design more effective algorithms to solve real-world big data analytical problems.

Solving non-convex/non-smooth economic load dispatch problems via an enhanced particle swarm optimization

Display Omitted An enhanced PSO is presented for solving non-convex/non-smooth ELD problems.A tent chaotic map is employed for the adaption of acceleration coefficients.A number of practical constraints are considered in the single-area and multi-area ELD problems.Experimental results of six cases demonstrate the effectiveness of the proposed PSO. Economic load dispatch (ELD) problems have been an important issue in optimal operation and planning of power system. Characterized by non-convex/non-smooth properties and various practical constraints, the ELD problems are difficult to solve using conventional optimization techniques. In this paper, an improved orthogonal design particle swarm optimization (IODPSO) algorithm is presented for solving the single-area and multi-area ELD problems with nonlinear characteristics of the generators, such as valve-point effects, prohibited operating zones, ramp rate limits and multiple fuels. In the IODPSO algorithm, an orthogonal designed method is used to construct a promising exemplar. Multiple auxiliary vector generating strategies are proposed to enhance the efficiency and effectiveness of orthogonal design operations. A tent chaotic map is employed for the adaptation of the acceleration coefficients, thus improving the proposed algorithms robustness and global search capabilities. In addition, we designed a repair method to handle the practical constraints. Six cases of ELD problems with different characteristics are utilized to benchmark the proposed algorithm. Experimental results demonstrate that IODPSO algorithm is a promising approach for solving the non-convex/non-smooth ELD problems.